ABSTRACT: The recent advancement in artificial intelligence (AI) based on machine learning (ML) has found unprecedented applications in many fields such as autonomous vehicle, precision medicine, financial market, and smart manufacturing. And deep machine learning hardware such as CNN can benefit from neuromorphic systems that are energy-efficient and biologically-inspired. In this talk we will highlight hardware-software synergy, nanoscale CMOS VLSI systems, and their applications for emulation of neuronal behaviors, synaptic interconnects, and neuromorphic computing. Historical perspective on Moore’s law, More than Moore’s, use of nonvolatile state variable, as in memristors both physical/ideal and extended, memristive nanoelectronics, their featured applications to both analog and digital VLSI systems such as processing-in-memory (PIM), low-power superdrivers will be also discussed.

BIO: Sung-Mo “Steve” Kang is a Distinguished Chair Professor of the Jack Baskin School of Engineering, UC Santa Cruz, and Chancellor Emeritus of UC Merced and President Emeritus of KAIST. He received his B.S. degree from Fairleigh Dickinson University, Teaneck, NJ, later an honorary B.S degree from Yonsei University of Korea, M.S. degree from the State University of New York at Buffalo, and Ph.D. degree from the University of California at Berkeley, all in electrical engineering. He holds 16 patents, published over 500 papers, and co-authored ten books. Prior to returning to academia, he led the development of world’s premier CMOS 32-bit VLSI microprocessor Bellmac-32 chipsets for telecommunication and computing applications as a technical supervisor of AT&T Bell laboratories, Murray Hill, NJ. For his research and education contributions, he has received honors, including the Silicon Valley Engineering Hall of Fame induction, Alexander von Humboldt Senior US Scientists Award, IEEE Millennium Medal, IEEE Mac Van Valkenburg CAS Society Award, IEEE CAS Technical Excellence Award, the US Semiconductor Research Corporation (SRC) Technical Excellence Award, IEEE Graduate Teaching Technical Field Award, IEEE CAS John Choma Education Award, Chang-Lin Tien Education Leadership Award, and distinguished alumnus awards from UC Berkeley, the University at Buffalo, Fairleigh Dickinson University, and Yonsei University. Dr. Kang is a Fellow of the IEEE, the Association for Computing Machinery (ACM), and the American Association for the Advancement of Science (AAAS). He is a member of Korean Academy of Science and Technology and a foreign member of National Academy of Engineering, Korea. His research interest includes modeling and simulation of semiconductor devices, memristive systems, low-power VLSI circuits, and neuromorphic computing.

Hosted by Prof Jason Cong and Prof Majid Sarrafzadeh